Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations23436
Missing cells352
Missing cells (%)< 0.1%
Duplicate rows15
Duplicate rows (%)0.1%
Total size in memory6.6 MiB
Average record size in memory296.0 B

Variable types

Numeric13
Categorical17
Unsupported3
Text3
Boolean1

Alerts

Dataset has 15 (0.1%) duplicate rowsDuplicates
Department is highly overall correlated with Education and 15 other fieldsHigh correlation
Education is highly overall correlated with Department and 4 other fieldsHigh correlation
EducationField is highly overall correlated with Department and 9 other fieldsHigh correlation
Employee Source is highly overall correlated with Department and 12 other fieldsHigh correlation
EnvironmentSatisfaction is highly overall correlated with Department and 12 other fieldsHigh correlation
Gender is highly overall correlated with Department and 18 other fieldsHigh correlation
JobInvolvement is highly overall correlated with Department and 12 other fieldsHigh correlation
JobLevel is highly overall correlated with JobRole and 1 other fieldsHigh correlation
JobRole is highly overall correlated with Department and 13 other fieldsHigh correlation
JobSatisfaction is highly overall correlated with Employee Source and 13 other fieldsHigh correlation
MaritalStatus is highly overall correlated with Employee Source and 13 other fieldsHigh correlation
NumCompaniesWorked is highly overall correlated with Department and 12 other fieldsHigh correlation
Over18 is highly overall correlated with Department and 18 other fieldsHigh correlation
OverTime is highly overall correlated with Department and 17 other fieldsHigh correlation
PercentSalaryHike is highly overall correlated with Department and 12 other fieldsHigh correlation
PerformanceRating is highly overall correlated with Department and 18 other fieldsHigh correlation
StandardHours is highly overall correlated with Department and 18 other fieldsHigh correlation
StockOptionLevel is highly overall correlated with Employee Source and 13 other fieldsHigh correlation
TotalWorkingYears is highly overall correlated with JobLevel and 1 other fieldsHigh correlation
TrainingTimesLastYear is highly overall correlated with Department and 12 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with Department and 8 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with Department and 7 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
Gender is highly imbalanced (51.4%)Imbalance
Over18 is highly imbalanced (99.9%)Imbalance
PerformanceRating is highly imbalanced (68.8%)Imbalance
StandardHours is highly imbalanced (99.9%)Imbalance
EnvironmentSatisfaction is highly skewed (γ1 = 108.2353204)Skewed
NumCompaniesWorked is highly skewed (γ1 = 144.5986932)Skewed
DistanceFromHome is an unsupported type, check if it needs cleaning or further analysisUnsupported
EmployeeCount is an unsupported type, check if it needs cleaning or further analysisUnsupported
Application ID is an unsupported type, check if it needs cleaning or further analysisUnsupported
NumCompaniesWorked has 3176 (13.6%) zerosZeros
TrainingTimesLastYear has 871 (3.7%) zerosZeros
YearsAtCompany has 740 (3.2%) zerosZeros
YearsInCurrentRole has 3925 (16.7%) zerosZeros
YearsSinceLastPromotion has 9271 (39.6%) zerosZeros
YearsWithCurrManager has 4197 (17.9%) zerosZeros

Reproduction

Analysis started2024-09-20 20:12:47.211619
Analysis finished2024-09-20 20:13:16.543524
Duration29.33 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct43
Distinct (%)0.2%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean36.936671
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:16.642756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1374318
Coefficient of variation (CV)0.24738104
Kurtosis-0.40829426
Mean36.936671
Median Absolute Deviation (MAD)6
Skewness0.41022185
Sum865537
Variance83.49266
MonotonicityNot monotonic
2024-09-20T17:13:16.789205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
34 1230
 
5.2%
35 1227
 
5.2%
36 1106
 
4.7%
31 1085
 
4.6%
29 1075
 
4.6%
32 984
 
4.2%
30 952
 
4.1%
38 945
 
4.0%
33 927
 
4.0%
40 893
 
3.8%
Other values (33) 13009
55.5%
ValueCountFrequency (%)
18 127
 
0.5%
19 143
 
0.6%
20 175
 
0.7%
21 213
 
0.9%
22 257
 
1.1%
23 223
 
1.0%
24 416
1.8%
25 416
1.8%
26 612
2.6%
27 773
3.3%
ValueCountFrequency (%)
60 80
 
0.3%
59 161
0.7%
58 224
1.0%
57 63
 
0.3%
56 223
1.0%
55 349
1.5%
54 287
1.2%
53 309
1.3%
52 288
1.2%
51 302
1.3%

Attrition
Categorical

Distinct2
Distinct (%)< 0.1%
Missing13
Missing (%)0.1%
Memory size183.2 KiB
Current employee
19714 
Voluntary Resignation
3709 

Length

Max length21
Median length16
Mean length16.791743
Min length16

Characters and Unicode

Total characters393313
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVoluntary Resignation
2nd rowVoluntary Resignation
3rd rowVoluntary Resignation
4th rowVoluntary Resignation
5th rowVoluntary Resignation

Common Values

ValueCountFrequency (%)
Current employee 19714
84.1%
Voluntary Resignation 3709
 
15.8%
(Missing) 13
 
0.1%

Length

2024-09-20T17:13:16.913942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:17.027430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
current 19714
42.1%
employee 19714
42.1%
voluntary 3709
 
7.9%
resignation 3709
 
7.9%

Most occurring characters

ValueCountFrequency (%)
e 82565
21.0%
r 43137
11.0%
n 30841
 
7.8%
t 27132
 
6.9%
o 27132
 
6.9%
u 23423
 
6.0%
y 23423
 
6.0%
23423
 
6.0%
l 23423
 
6.0%
C 19714
 
5.0%
Other values (8) 69100
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 393313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 82565
21.0%
r 43137
11.0%
n 30841
 
7.8%
t 27132
 
6.9%
o 27132
 
6.9%
u 23423
 
6.0%
y 23423
 
6.0%
23423
 
6.0%
l 23423
 
6.0%
C 19714
 
5.0%
Other values (8) 69100
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 393313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 82565
21.0%
r 43137
11.0%
n 30841
 
7.8%
t 27132
 
6.9%
o 27132
 
6.9%
u 23423
 
6.0%
y 23423
 
6.0%
23423
 
6.0%
l 23423
 
6.0%
C 19714
 
5.0%
Other values (8) 69100
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 393313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 82565
21.0%
r 43137
11.0%
n 30841
 
7.8%
t 27132
 
6.9%
o 27132
 
6.9%
u 23423
 
6.0%
y 23423
 
6.0%
23423
 
6.0%
l 23423
 
6.0%
C 19714
 
5.0%
Other values (8) 69100
17.6%

BusinessTravel
Categorical

Distinct3
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size183.2 KiB
Travel_Rarely
16620 
Travel_Frequently
4413 
Non-Travel
2395 

Length

Max length17
Median length13
Mean length13.446773
Min length10

Characters and Unicode

Total characters315031
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Rarely
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 16620
70.9%
Travel_Frequently 4413
 
18.8%
Non-Travel 2395
 
10.2%
(Missing) 8
 
< 0.1%

Length

2024-09-20T17:13:17.143666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:17.253544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 16620
70.9%
travel_frequently 4413
 
18.8%
non-travel 2395
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e 48874
15.5%
l 44461
14.1%
r 44461
14.1%
a 40048
12.7%
T 23428
7.4%
v 23428
7.4%
_ 21033
6.7%
y 21033
6.7%
R 16620
 
5.3%
n 6808
 
2.2%
Other values (7) 24837
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 315031
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 48874
15.5%
l 44461
14.1%
r 44461
14.1%
a 40048
12.7%
T 23428
7.4%
v 23428
7.4%
_ 21033
6.7%
y 21033
6.7%
R 16620
 
5.3%
n 6808
 
2.2%
Other values (7) 24837
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 315031
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 48874
15.5%
l 44461
14.1%
r 44461
14.1%
a 40048
12.7%
T 23428
7.4%
v 23428
7.4%
_ 21033
6.7%
y 21033
6.7%
R 16620
 
5.3%
n 6808
 
2.2%
Other values (7) 24837
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 315031
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 48874
15.5%
l 44461
14.1%
r 44461
14.1%
a 40048
12.7%
T 23428
7.4%
v 23428
7.4%
_ 21033
6.7%
y 21033
6.7%
R 16620
 
5.3%
n 6808
 
2.2%
Other values (7) 24837
7.9%

DailyRate
Real number (ℝ)

Distinct883
Distinct (%)3.8%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean801.82877
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:17.372959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile164
Q1465
median802
Q31157
95-th percentile1423
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.20617
Coefficient of variation (CV)0.5028582
Kurtosis-1.2047853
Mean801.82877
Median Absolute Deviation (MAD)344
Skewness-0.0046440396
Sum18782037
Variance162575.21
MonotonicityNot monotonic
2024-09-20T17:13:17.510476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 99
 
0.4%
408 81
 
0.3%
1329 80
 
0.3%
530 79
 
0.3%
1082 79
 
0.3%
329 79
 
0.3%
427 68
 
0.3%
334 67
 
0.3%
950 65
 
0.3%
1125 65
 
0.3%
Other values (873) 22662
96.7%
ValueCountFrequency (%)
102 16
 
0.1%
103 18
 
0.1%
104 16
 
0.1%
105 15
 
0.1%
106 16
 
0.1%
107 16
 
0.1%
109 15
 
0.1%
111 47
0.2%
115 16
 
0.1%
116 32
0.1%
ValueCountFrequency (%)
1499 15
 
0.1%
1498 16
 
0.1%
1496 32
0.1%
1495 48
0.2%
1492 16
 
0.1%
1490 62
0.3%
1488 16
 
0.1%
1485 46
0.2%
1482 16
 
0.1%
1480 32
0.1%

Department
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing11
Missing (%)< 0.1%
Memory size183.2 KiB
Research & Development
15286 
Sales
7119 
Human Resources
 
1019
1296
 
1

Length

Max length22
Median length22
Mean length16.528324
Min length4

Characters and Unicode

Total characters387176
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSales
2nd rowSales
3rd rowSales
4th rowSales
5th rowSales

Common Values

ValueCountFrequency (%)
Research & Development 15286
65.2%
Sales 7119
30.4%
Human Resources 1019
 
4.3%
1296 1
 
< 0.1%
(Missing) 11
 
< 0.1%

Length

2024-09-20T17:13:17.676860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:17.786992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
research 15286
27.8%
15286
27.8%
development 15286
27.8%
sales 7119
12.9%
human 1019
 
1.9%
resources 1019
 
1.9%
1296 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 85587
22.1%
31591
 
8.2%
s 24443
 
6.3%
a 23424
 
6.0%
l 22405
 
5.8%
R 16305
 
4.2%
c 16305
 
4.2%
r 16305
 
4.2%
m 16305
 
4.2%
n 16305
 
4.2%
Other values (14) 118201
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 387176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 85587
22.1%
31591
 
8.2%
s 24443
 
6.3%
a 23424
 
6.0%
l 22405
 
5.8%
R 16305
 
4.2%
c 16305
 
4.2%
r 16305
 
4.2%
m 16305
 
4.2%
n 16305
 
4.2%
Other values (14) 118201
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 387176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 85587
22.1%
31591
 
8.2%
s 24443
 
6.3%
a 23424
 
6.0%
l 22405
 
5.8%
R 16305
 
4.2%
c 16305
 
4.2%
r 16305
 
4.2%
m 16305
 
4.2%
n 16305
 
4.2%
Other values (14) 118201
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 387176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 85587
22.1%
31591
 
8.2%
s 24443
 
6.3%
a 23424
 
6.0%
l 22405
 
5.8%
R 16305
 
4.2%
c 16305
 
4.2%
r 16305
 
4.2%
m 16305
 
4.2%
n 16305
 
4.2%
Other values (14) 118201
30.5%

DistanceFromHome
Unsupported

REJECTED  UNSUPPORTED 

Missing9
Missing (%)< 0.1%
Memory size183.2 KiB

Education
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.9100495
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:17.906317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.024931
Coefficient of variation (CV)0.35220396
Kurtosis-0.56488289
Mean2.9100495
Median Absolute Deviation (MAD)1
Skewness-0.28426054
Sum68165
Variance1.0504835
MonotonicityNot monotonic
2024-09-20T17:13:18.026706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 9098
38.8%
4 6321
27.0%
2 4517
19.3%
1 2722
 
11.6%
5 765
 
3.3%
6 1
 
< 0.1%
(Missing) 12
 
0.1%
ValueCountFrequency (%)
1 2722
 
11.6%
2 4517
19.3%
3 9098
38.8%
4 6321
27.0%
5 765
 
3.3%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 765
 
3.3%
4 6321
27.0%
3 9098
38.8%
2 4517
19.3%
1 2722
 
11.6%

EducationField
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Memory size183.2 KiB
Life Sciences
9701 
Medical
7337 
Marketing
2541 
Technical Degree
2089 
Other
1311 
Other values (3)
 
448

Length

Max length16
Median length15
Mean length10.54403
Min length1

Characters and Unicode

Total characters247015
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowLife Sciences
2nd rowLife Sciences
3rd rowLife Sciences
4th rowLife Sciences
5th rowLife Sciences

Common Values

ValueCountFrequency (%)
Life Sciences 9701
41.4%
Medical 7337
31.3%
Marketing 2541
 
10.8%
Technical Degree 2089
 
8.9%
Other 1311
 
5.6%
Human Resources 446
 
1.9%
3 1
 
< 0.1%
Test 1
 
< 0.1%
(Missing) 9
 
< 0.1%

Length

2024-09-20T17:13:18.156526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:18.276882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
life 9701
27.2%
sciences 9701
27.2%
medical 7337
20.6%
marketing 2541
 
7.1%
technical 2089
 
5.9%
degree 2089
 
5.9%
other 1311
 
3.7%
human 446
 
1.3%
resources 446
 
1.3%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 49541
20.1%
i 31369
12.7%
c 31363
12.7%
n 14777
 
6.0%
a 12413
 
5.0%
12236
 
5.0%
s 10594
 
4.3%
M 9878
 
4.0%
f 9701
 
3.9%
S 9701
 
3.9%
Other values (17) 55442
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 247015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 49541
20.1%
i 31369
12.7%
c 31363
12.7%
n 14777
 
6.0%
a 12413
 
5.0%
12236
 
5.0%
s 10594
 
4.3%
M 9878
 
4.0%
f 9701
 
3.9%
S 9701
 
3.9%
Other values (17) 55442
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 247015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 49541
20.1%
i 31369
12.7%
c 31363
12.7%
n 14777
 
6.0%
a 12413
 
5.0%
12236
 
5.0%
s 10594
 
4.3%
M 9878
 
4.0%
f 9701
 
3.9%
S 9701
 
3.9%
Other values (17) 55442
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 247015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 49541
20.1%
i 31369
12.7%
c 31363
12.7%
n 14777
 
6.0%
a 12413
 
5.0%
12236
 
5.0%
s 10594
 
4.3%
M 9878
 
4.0%
f 9701
 
3.9%
S 9701
 
3.9%
Other values (17) 55442
22.4%

EmployeeCount
Unsupported

REJECTED  UNSUPPORTED 

Missing5
Missing (%)< 0.1%
Memory size183.2 KiB
Distinct23366
Distinct (%)99.7%
Missing1
Missing (%)< 0.1%
Memory size183.2 KiB
2024-09-20T17:13:18.876635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.5260081
Min length1

Characters and Unicode

Total characters106067
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23327 ?
Unique (%)99.5%

Sample

1st row1
2nd row1
3rd row7
4th row8
5th row9
ValueCountFrequency (%)
1 7
 
< 0.1%
23244 7
 
< 0.1%
6325 6
 
< 0.1%
10442 5
 
< 0.1%
12078 4
 
< 0.1%
9568 4
 
< 0.1%
12686 4
 
< 0.1%
10024 4
 
< 0.1%
35 4
 
< 0.1%
17031 3
 
< 0.1%
Other values (23355) 23387
99.8%
2024-09-20T17:13:19.443243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 20056
18.9%
2 13531
12.8%
3 9577
9.0%
4 9040
8.5%
5 8993
8.5%
0 8991
8.5%
6 8990
8.5%
8 8965
8.5%
7 8962
8.4%
9 8947
8.4%
Other values (9) 15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 20056
18.9%
2 13531
12.8%
3 9577
9.0%
4 9040
8.5%
5 8993
8.5%
0 8991
8.5%
6 8990
8.5%
8 8965
8.5%
7 8962
8.4%
9 8947
8.4%
Other values (9) 15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 20056
18.9%
2 13531
12.8%
3 9577
9.0%
4 9040
8.5%
5 8993
8.5%
0 8991
8.5%
6 8990
8.5%
8 8965
8.5%
7 8962
8.4%
9 8947
8.4%
Other values (9) 15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 20056
18.9%
2 13531
12.8%
3 9577
9.0%
4 9040
8.5%
5 8993
8.5%
0 8991
8.5%
6 8990
8.5%
8 8965
8.5%
7 8962
8.4%
9 8947
8.4%
Other values (9) 15
 
< 0.1%

Application ID
Unsupported

REJECTED  UNSUPPORTED 

Missing3
Missing (%)< 0.1%
Memory size183.2 KiB

EnvironmentSatisfaction
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13.681777
Minimum1
Maximum129588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:19.576791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum129588
Range129587
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1186.5444
Coefficient of variation (CV)86.724432
Kurtosis11714.866
Mean13.681777
Median Absolute Deviation (MAD)1
Skewness108.23532
Sum320523
Variance1407887.5
MonotonicityNot monotonic
2024-09-20T17:13:19.680987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 7196
30.7%
4 7110
30.3%
1 4580
19.5%
2 4539
19.4%
127249 1
 
< 0.1%
129588 1
 
< 0.1%
(Missing) 9
 
< 0.1%
ValueCountFrequency (%)
1 4580
19.5%
2 4539
19.4%
3 7196
30.7%
4 7110
30.3%
127249 1
 
< 0.1%
129588 1
 
< 0.1%
ValueCountFrequency (%)
129588 1
 
< 0.1%
127249 1
 
< 0.1%
4 7110
30.3%
3 7196
30.7%
2 4539
19.4%
1 4580
19.5%

Gender
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Memory size183.2 KiB
Male
14056 
Female
9368 
1
 
1
2
 
1

Length

Max length6
Median length4
Mean length4.799539
Min length1

Characters and Unicode

Total characters112434
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 14056
60.0%
Female 9368
40.0%
1 1
 
< 0.1%
2 1
 
< 0.1%
(Missing) 10
 
< 0.1%

Length

2024-09-20T17:13:19.804898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:19.910392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 14056
60.0%
female 9368
40.0%
1 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 32792
29.2%
a 23424
20.8%
l 23424
20.8%
M 14056
12.5%
F 9368
 
8.3%
m 9368
 
8.3%
1 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 32792
29.2%
a 23424
20.8%
l 23424
20.8%
M 14056
12.5%
F 9368
 
8.3%
m 9368
 
8.3%
1 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 32792
29.2%
a 23424
20.8%
l 23424
20.8%
M 14056
12.5%
F 9368
 
8.3%
m 9368
 
8.3%
1 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 32792
29.2%
a 23424
20.8%
l 23424
20.8%
M 14056
12.5%
F 9368
 
8.3%
m 9368
 
8.3%
1 1
 
< 0.1%
2 1
 
< 0.1%
Distinct73
Distinct (%)0.3%
Missing9
Missing (%)< 0.1%
Memory size183.2 KiB
2024-09-20T17:13:20.176764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.0128484
Min length2

Characters and Unicode

Total characters47155
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row94
2nd row94
3rd row94
4th row94
5th row94
ValueCountFrequency (%)
66 480
 
2.0%
48 447
 
1.9%
98 447
 
1.9%
42 445
 
1.9%
84 444
 
1.9%
96 436
 
1.9%
79 431
 
1.8%
87 417
 
1.8%
57 416
 
1.8%
56 410
 
1.8%
Other values (63) 19054
81.3%
2024-09-20T17:13:20.569078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 5798
12.3%
4 5789
12.3%
8 5760
12.2%
7 5744
12.2%
9 5640
12.0%
5 5502
11.7%
3 4958
10.5%
2 2799
5.9%
0 2698
5.7%
1 2457
5.2%
Other values (6) 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47155
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 5798
12.3%
4 5789
12.3%
8 5760
12.2%
7 5744
12.2%
9 5640
12.0%
5 5502
11.7%
3 4958
10.5%
2 2799
5.9%
0 2698
5.7%
1 2457
5.2%
Other values (6) 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47155
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 5798
12.3%
4 5789
12.3%
8 5760
12.2%
7 5744
12.2%
9 5640
12.0%
5 5502
11.7%
3 4958
10.5%
2 2799
5.9%
0 2698
5.7%
1 2457
5.2%
Other values (6) 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47155
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 5798
12.3%
4 5789
12.3%
8 5760
12.2%
7 5744
12.2%
9 5640
12.0%
5 5502
11.7%
3 4958
10.5%
2 2799
5.9%
0 2698
5.7%
1 2457
5.2%
Other values (6) 10
 
< 0.1%

JobInvolvement
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.7338114
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:20.690345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum54
Range53
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.83686021
Coefficient of variation (CV)0.30611483
Kurtosis934.31609
Mean2.7338114
Median Absolute Deviation (MAD)0
Skewness15.816353
Sum64045
Variance0.70033501
MonotonicityNot monotonic
2024-09-20T17:13:20.793500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 13853
59.1%
2 5973
25.5%
4 2280
 
9.7%
1 1319
 
5.6%
47 1
 
< 0.1%
54 1
 
< 0.1%
(Missing) 9
 
< 0.1%
ValueCountFrequency (%)
1 1319
 
5.6%
2 5973
25.5%
3 13853
59.1%
4 2280
 
9.7%
47 1
 
< 0.1%
54 1
 
< 0.1%
ValueCountFrequency (%)
54 1
 
< 0.1%
47 1
 
< 0.1%
4 2280
 
9.7%
3 13853
59.1%
2 5973
25.5%
1 1319
 
5.6%

JobLevel
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size183.2 KiB
1.0
8641 
2.0
8526 
3.0
3475 
4.0
1695 
5.0
1092 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters70287
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 8641
36.9%
2.0 8526
36.4%
3.0 3475
14.8%
4.0 1695
 
7.2%
5.0 1092
 
4.7%
(Missing) 7
 
< 0.1%

Length

2024-09-20T17:13:20.910393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:21.010028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8641
36.9%
2.0 8526
36.4%
3.0 3475
14.8%
4.0 1695
 
7.2%
5.0 1092
 
4.7%

Most occurring characters

ValueCountFrequency (%)
. 23429
33.3%
0 23429
33.3%
1 8641
 
12.3%
2 8526
 
12.1%
3 3475
 
4.9%
4 1695
 
2.4%
5 1092
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 23429
33.3%
0 23429
33.3%
1 8641
 
12.3%
2 8526
 
12.1%
3 3475
 
4.9%
4 1695
 
2.4%
5 1092
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 23429
33.3%
0 23429
33.3%
1 8641
 
12.3%
2 8526
 
12.1%
3 3475
 
4.9%
4 1695
 
2.4%
5 1092
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 23429
33.3%
0 23429
33.3%
1 8641
 
12.3%
2 8526
 
12.1%
3 3475
 
4.9%
4 1695
 
2.4%
5 1092
 
1.6%

JobRole
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Memory size183.2 KiB
Sales Executive
5111 
Research Scientist
4634 
Laboratory Technician
4162 
Manufacturing Director
2376 
Healthcare Representative
2104 
Other values (6)
5040 

Length

Max length25
Median length21
Mean length18.108465
Min length1

Characters and Unicode

Total characters424227
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowSales Executive
2nd rowSales Executive
3rd rowSales Executive
4th rowSales Executive
5th rowSales Executive

Common Values

ValueCountFrequency (%)
Sales Executive 5111
21.8%
Research Scientist 4634
19.8%
Laboratory Technician 4162
17.8%
Manufacturing Director 2376
10.1%
Healthcare Representative 2104
9.0%
Manager 1600
 
6.8%
Sales Representative 1306
 
5.6%
Research Director 1287
 
5.5%
Human Resources 845
 
3.6%
5 1
 
< 0.1%
(Missing) 9
 
< 0.1%

Length

2024-09-20T17:13:21.143365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sales 6417
14.2%
research 5921
13.1%
executive 5111
11.3%
scientist 4634
10.2%
laboratory 4162
9.2%
technician 4162
9.2%
director 3663
8.1%
representative 3410
7.5%
manufacturing 2376
 
5.3%
healthcare 2104
 
4.6%
Other values (5) 3292
7.3%

Most occurring characters

ValueCountFrequency (%)
e 62078
14.6%
a 41239
 
9.7%
t 33504
 
7.9%
c 32978
 
7.8%
i 32152
 
7.6%
r 31906
 
7.5%
n 23565
 
5.6%
s 22072
 
5.2%
21825
 
5.1%
o 12832
 
3.0%
Other values (21) 110076
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 424227
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 62078
14.6%
a 41239
 
9.7%
t 33504
 
7.9%
c 32978
 
7.8%
i 32152
 
7.6%
r 31906
 
7.5%
n 23565
 
5.6%
s 22072
 
5.2%
21825
 
5.1%
o 12832
 
3.0%
Other values (21) 110076
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 424227
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 62078
14.6%
a 41239
 
9.7%
t 33504
 
7.9%
c 32978
 
7.8%
i 32152
 
7.6%
r 31906
 
7.5%
n 23565
 
5.6%
s 22072
 
5.2%
21825
 
5.1%
o 12832
 
3.0%
Other values (21) 110076
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 424227
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 62078
14.6%
a 41239
 
9.7%
t 33504
 
7.9%
c 32978
 
7.8%
i 32152
 
7.6%
r 31906
 
7.5%
n 23565
 
5.6%
s 22072
 
5.2%
21825
 
5.1%
o 12832
 
3.0%
Other values (21) 110076
25.9%

JobSatisfaction
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Memory size183.2 KiB
4
7276 
3
7088 
1
4605 
2
4456 
Manager
 
2

Length

Max length7
Median length1
Mean length1.0005122
Min length1

Characters and Unicode

Total characters23439
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 7276
31.0%
3 7088
30.2%
1 4605
19.6%
2 4456
19.0%
Manager 2
 
< 0.1%
(Missing) 9
 
< 0.1%

Length

2024-09-20T17:13:21.291171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:21.425339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 7276
31.1%
3 7088
30.3%
1 4605
19.7%
2 4456
19.0%
manager 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 7276
31.0%
3 7088
30.2%
1 4605
19.6%
2 4456
19.0%
a 4
 
< 0.1%
M 2
 
< 0.1%
n 2
 
< 0.1%
g 2
 
< 0.1%
e 2
 
< 0.1%
r 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 7276
31.0%
3 7088
30.2%
1 4605
19.6%
2 4456
19.0%
a 4
 
< 0.1%
M 2
 
< 0.1%
n 2
 
< 0.1%
g 2
 
< 0.1%
e 2
 
< 0.1%
r 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 7276
31.0%
3 7088
30.2%
1 4605
19.6%
2 4456
19.0%
a 4
 
< 0.1%
M 2
 
< 0.1%
n 2
 
< 0.1%
g 2
 
< 0.1%
e 2
 
< 0.1%
r 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 7276
31.0%
3 7088
30.2%
1 4605
19.6%
2 4456
19.0%
a 4
 
< 0.1%
M 2
 
< 0.1%
n 2
 
< 0.1%
g 2
 
< 0.1%
e 2
 
< 0.1%
r 2
 
< 0.1%

MaritalStatus
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing11
Missing (%)< 0.1%
Memory size183.2 KiB
Married
10709 
Single
7504 
Divorced
5210 
4
 
2

Length

Max length8
Median length7
Mean length6.9015582
Min length1

Characters and Unicode

Total characters161669
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 10709
45.7%
Single 7504
32.0%
Divorced 5210
22.2%
4 2
 
< 0.1%
(Missing) 11
 
< 0.1%

Length

2024-09-20T17:13:21.554781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:21.660120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
married 10709
45.7%
single 7504
32.0%
divorced 5210
22.2%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 26628
16.5%
e 23423
14.5%
i 23423
14.5%
d 15919
9.8%
M 10709
6.6%
a 10709
6.6%
S 7504
 
4.6%
n 7504
 
4.6%
g 7504
 
4.6%
l 7504
 
4.6%
Other values (5) 20842
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 161669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 26628
16.5%
e 23423
14.5%
i 23423
14.5%
d 15919
9.8%
M 10709
6.6%
a 10709
6.6%
S 7504
 
4.6%
n 7504
 
4.6%
g 7504
 
4.6%
l 7504
 
4.6%
Other values (5) 20842
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 161669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 26628
16.5%
e 23423
14.5%
i 23423
14.5%
d 15919
9.8%
M 10709
6.6%
a 10709
6.6%
S 7504
 
4.6%
n 7504
 
4.6%
g 7504
 
4.6%
l 7504
 
4.6%
Other values (5) 20842
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 161669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 26628
16.5%
e 23423
14.5%
i 23423
14.5%
d 15919
9.8%
M 10709
6.6%
a 10709
6.6%
S 7504
 
4.6%
n 7504
 
4.6%
g 7504
 
4.6%
l 7504
 
4.6%
Other values (5) 20842
12.9%
Distinct1351
Distinct (%)5.8%
Missing13
Missing (%)0.1%
Memory size183.2 KiB
2024-09-20T17:13:22.057290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.1909661
Min length4

Characters and Unicode

Total characters98165
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row5993
2nd row5993
3rd row5993
4th row5993
5th row5993
ValueCountFrequency (%)
2342 66
 
0.3%
2559 54
 
0.2%
2380 49
 
0.2%
2741 48
 
0.2%
6347 48
 
0.2%
2610 48
 
0.2%
5562 48
 
0.2%
6142 47
 
0.2%
2451 47
 
0.2%
3452 46
 
0.2%
Other values (1341) 22922
97.9%
2024-09-20T17:13:22.610399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 13215
13.5%
1 11715
11.9%
4 11000
11.2%
3 10288
10.5%
6 9525
9.7%
5 9348
9.5%
7 8688
8.9%
9 8436
8.6%
0 8235
8.4%
8 7702
7.8%
Other values (10) 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 13215
13.5%
1 11715
11.9%
4 11000
11.2%
3 10288
10.5%
6 9525
9.7%
5 9348
9.5%
7 8688
8.9%
9 8436
8.6%
0 8235
8.4%
8 7702
7.8%
Other values (10) 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 13215
13.5%
1 11715
11.9%
4 11000
11.2%
3 10288
10.5%
6 9525
9.7%
5 9348
9.5%
7 8688
8.9%
9 8436
8.6%
0 8235
8.4%
8 7702
7.8%
Other values (10) 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 13215
13.5%
1 11715
11.9%
4 11000
11.2%
3 10288
10.5%
6 9525
9.7%
5 9348
9.5%
7 8688
8.9%
9 8436
8.6%
0 8235
8.4%
8 7702
7.8%
Other values (10) 13
 
< 0.1%

MonthlyRate
Real number (ℝ)

Distinct1429
Distinct (%)6.1%
Missing11
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14304.344
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:22.771868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3395
Q18053
median14222
Q320460
95-th percentile25412
Maximum26999
Range24905
Interquartile range (IQR)12407

Descriptive statistics

Standard deviation7102.6363
Coefficient of variation (CV)0.49653702
Kurtosis-1.2148608
Mean14304.344
Median Absolute Deviation (MAD)6204
Skewness0.019498803
Sum3.3507925 × 108
Variance50447442
MonotonicityNot monotonic
2024-09-20T17:13:22.910327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9150 53
 
0.2%
4223 49
 
0.2%
19373 34
 
0.1%
21534 33
 
0.1%
22074 32
 
0.1%
11652 32
 
0.1%
15891 32
 
0.1%
8952 32
 
0.1%
2125 32
 
0.1%
24444 32
 
0.1%
Other values (1419) 23064
98.4%
ValueCountFrequency (%)
2094 16
0.1%
2097 15
0.1%
2104 16
0.1%
2112 15
0.1%
2122 16
0.1%
2125 32
0.1%
2137 16
0.1%
2227 16
0.1%
2243 16
0.1%
2253 16
0.1%
ValueCountFrequency (%)
26999 16
0.1%
26997 14
0.1%
26968 16
0.1%
26959 15
0.1%
26956 16
0.1%
26933 16
0.1%
26914 16
0.1%
26897 15
0.1%
26894 15
0.1%
26862 16
0.1%

NumCompaniesWorked
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct12
Distinct (%)0.1%
Missing9
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.8877791
Minimum0
Maximum23258
Zeros3176
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:23.027128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum23258
Range23258
Interquartile range (IQR)3

Descriptive statistics

Standard deviation155.3329
Coefficient of variation (CV)39.954149
Kurtosis21486.887
Mean3.8877791
Median Absolute Deviation (MAD)1
Skewness144.59869
Sum91079
Variance24128.311
MonotonicityNot monotonic
2024-09-20T17:13:23.126791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 8311
35.5%
0 3176
 
13.6%
3 2508
 
10.7%
2 2330
 
9.9%
4 2208
 
9.4%
7 1171
 
5.0%
6 1108
 
4.7%
5 1002
 
4.3%
9 818
 
3.5%
8 793
 
3.4%
Other values (2) 2
 
< 0.1%
(Missing) 9
 
< 0.1%
ValueCountFrequency (%)
0 3176
 
13.6%
1 8311
35.5%
2 2330
 
9.9%
3 2508
 
10.7%
4 2208
 
9.4%
5 1002
 
4.3%
6 1108
 
4.7%
7 1171
 
5.0%
8 793
 
3.4%
9 818
 
3.5%
ValueCountFrequency (%)
23258 1
 
< 0.1%
4933 1
 
< 0.1%
9 818
 
3.5%
8 793
 
3.4%
7 1171
5.0%
6 1108
4.7%
5 1002
 
4.3%
4 2208
9.4%
3 2508
10.7%
2 2330
9.9%

Over18
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Memory size183.2 KiB
Y
23424 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23426
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 23424
99.9%
1 2
 
< 0.1%
(Missing) 10
 
< 0.1%

Length

2024-09-20T17:13:23.243445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:23.343630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
y 23424
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
Y 23424
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 23424
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 23424
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 23424
> 99.9%
1 2
 
< 0.1%

OverTime
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing12
Missing (%)0.1%
Memory size45.9 KiB
False
16790 
True
6634 
(Missing)
 
12
ValueCountFrequency (%)
False 16790
71.6%
True 6634
 
28.3%
(Missing) 12
 
0.1%
2024-09-20T17:13:23.426980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

PercentSalaryHike
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.1%
Missing14
Missing (%)0.1%
Memory size183.2 KiB
11
3353 
13
3345 
14
3216 
12
3125 
15
1596 
Other values (12)
8787 

Length

Max length3
Median length2
Mean length2.0000427
Min length2

Characters and Unicode

Total characters46845
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 3353
14.3%
13 3345
14.3%
14 3216
13.7%
12 3125
13.3%
15 1596
6.8%
18 1408
6.0%
17 1312
 
5.6%
16 1247
 
5.3%
19 1214
 
5.2%
22 889
 
3.8%
Other values (7) 2717
11.6%

Length

2024-09-20T17:13:23.693607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11 3353
14.3%
13 3345
14.3%
14 3216
13.7%
12 3125
13.3%
15 1596
6.8%
18 1408
6.0%
17 1312
 
5.6%
16 1247
 
5.3%
19 1214
 
5.2%
22 889
 
3.8%
Other values (7) 2717
11.6%

Most occurring characters

ValueCountFrequency (%)
1 23936
51.1%
2 7618
 
16.3%
3 3790
 
8.1%
4 3556
 
7.6%
5 1879
 
4.0%
8 1408
 
3.0%
7 1312
 
2.8%
6 1247
 
2.7%
9 1214
 
2.6%
0 880
 
1.9%
Other values (5) 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46845
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23936
51.1%
2 7618
 
16.3%
3 3790
 
8.1%
4 3556
 
7.6%
5 1879
 
4.0%
8 1408
 
3.0%
7 1312
 
2.8%
6 1247
 
2.7%
9 1214
 
2.6%
0 880
 
1.9%
Other values (5) 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46845
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23936
51.1%
2 7618
 
16.3%
3 3790
 
8.1%
4 3556
 
7.6%
5 1879
 
4.0%
8 1408
 
3.0%
7 1312
 
2.8%
6 1247
 
2.7%
9 1214
 
2.6%
0 880
 
1.9%
Other values (5) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46845
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23936
51.1%
2 7618
 
16.3%
3 3790
 
8.1%
4 3556
 
7.6%
5 1879
 
4.0%
8 1408
 
3.0%
7 1312
 
2.8%
6 1247
 
2.7%
9 1214
 
2.6%
0 880
 
1.9%
Other values (5) 5
 
< 0.1%

PerformanceRating
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Memory size183.2 KiB
3.0
19791 
4.0
3633 
11.0
 
1
13.0
 
1

Length

Max length4
Median length3
Mean length3.0000854
Min length3

Characters and Unicode

Total characters70280
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 19791
84.4%
4.0 3633
 
15.5%
11.0 1
 
< 0.1%
13.0 1
 
< 0.1%
(Missing) 10
 
< 0.1%

Length

2024-09-20T17:13:23.810129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:23.910294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 19791
84.5%
4.0 3633
 
15.5%
11.0 1
 
< 0.1%
13.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 23426
33.3%
0 23426
33.3%
3 19792
28.2%
4 3633
 
5.2%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 23426
33.3%
0 23426
33.3%
3 19792
28.2%
4 3633
 
5.2%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 23426
33.3%
0 23426
33.3%
3 19792
28.2%
4 3633
 
5.2%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 23426
33.3%
0 23426
33.3%
3 19792
28.2%
4 3633
 
5.2%
1 3
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size183.2 KiB
3.0
7316 
4.0
6888 
2.0
4844 
1.0
4380 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters70284
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 7316
31.2%
4.0 6888
29.4%
2.0 4844
20.7%
1.0 4380
18.7%
(Missing) 8
 
< 0.1%

Length

2024-09-20T17:13:24.026814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:24.126800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 7316
31.2%
4.0 6888
29.4%
2.0 4844
20.7%
1.0 4380
18.7%

Most occurring characters

ValueCountFrequency (%)
. 23428
33.3%
0 23428
33.3%
3 7316
 
10.4%
4 6888
 
9.8%
2 4844
 
6.9%
1 4380
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70284
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 23428
33.3%
0 23428
33.3%
3 7316
 
10.4%
4 6888
 
9.8%
2 4844
 
6.9%
1 4380
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70284
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 23428
33.3%
0 23428
33.3%
3 7316
 
10.4%
4 6888
 
9.8%
2 4844
 
6.9%
1 4380
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70284
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 23428
33.3%
0 23428
33.3%
3 7316
 
10.4%
4 6888
 
9.8%
2 4844
 
6.9%
1 4380
 
6.2%

StandardHours
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Memory size183.2 KiB
80.0
23424 
4.0
 
1
3.0
 
1

Length

Max length4
Median length4
Mean length3.9999146
Min length3

Characters and Unicode

Total characters93702
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row80.0
2nd row80.0
3rd row80.0
4th row80.0
5th row80.0

Common Values

ValueCountFrequency (%)
80.0 23424
99.9%
4.0 1
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 10
 
< 0.1%

Length

2024-09-20T17:13:24.243461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:24.343235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
80.0 23424
> 99.9%
4.0 1
 
< 0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 46850
50.0%
. 23426
25.0%
8 23424
25.0%
4 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 46850
50.0%
. 23426
25.0%
8 23424
25.0%
4 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 46850
50.0%
. 23426
25.0%
8 23424
25.0%
4 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 46850
50.0%
. 23426
25.0%
8 23424
25.0%
4 1
 
< 0.1%
3 1
 
< 0.1%

StockOptionLevel
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Memory size183.2 KiB
0.0
10066 
1.0
9483 
2.0
2533 
3.0
1343 
80.0
 
2

Length

Max length4
Median length3
Mean length3.0000854
Min length3

Characters and Unicode

Total characters70283
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10066
43.0%
1.0 9483
40.5%
2.0 2533
 
10.8%
3.0 1343
 
5.7%
80.0 2
 
< 0.1%
(Missing) 9
 
< 0.1%

Length

2024-09-20T17:13:24.466414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:24.576133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10066
43.0%
1.0 9483
40.5%
2.0 2533
 
10.8%
3.0 1343
 
5.7%
80.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 33495
47.7%
. 23427
33.3%
1 9483
 
13.5%
2 2533
 
3.6%
3 1343
 
1.9%
8 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 33495
47.7%
. 23427
33.3%
1 9483
 
13.5%
2 2533
 
3.6%
3 1343
 
1.9%
8 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 33495
47.7%
. 23427
33.3%
1 9483
 
13.5%
2 2533
 
3.6%
3 1343
 
1.9%
8 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 33495
47.7%
. 23427
33.3%
1 9483
 
13.5%
2 2533
 
3.6%
3 1343
 
1.9%
8 2
 
< 0.1%

TotalWorkingYears
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)0.2%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11.25922
Minimum0
Maximum40
Zeros211
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:24.695463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7723697
Coefficient of variation (CV)0.69031157
Kurtosis0.92676957
Mean11.25922
Median Absolute Deviation (MAD)4
Skewness1.1172516
Sum263781
Variance60.409731
MonotonicityNot monotonic
2024-09-20T17:13:24.843345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 3241
 
13.8%
6 1992
 
8.5%
8 1653
 
7.1%
9 1538
 
6.6%
5 1396
 
6.0%
7 1285
 
5.5%
1 1284
 
5.5%
4 992
 
4.2%
12 757
 
3.2%
3 670
 
2.9%
Other values (30) 8620
36.8%
ValueCountFrequency (%)
0 211
 
0.9%
1 1284
5.5%
2 485
 
2.1%
3 670
 
2.9%
4 992
4.2%
5 1396
6.0%
6 1992
8.5%
7 1285
5.5%
8 1653
7.1%
9 1538
6.6%
ValueCountFrequency (%)
40 37
 
0.2%
38 16
 
0.1%
37 56
 
0.2%
36 92
0.4%
35 46
 
0.2%
34 82
0.3%
33 110
0.5%
32 150
0.6%
31 148
0.6%
30 105
0.4%

TrainingTimesLastYear
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing11
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.8001708
Minimum0
Maximum30
Zeros871
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:24.963422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3085273
Coefficient of variation (CV)0.4673027
Kurtosis10.249696
Mean2.8001708
Median Absolute Deviation (MAD)1
Skewness1.0425426
Sum65594
Variance1.7122438
MonotonicityNot monotonic
2024-09-20T17:13:25.076791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 8725
37.2%
3 7806
33.3%
4 1970
 
8.4%
5 1882
 
8.0%
1 1126
 
4.8%
6 1043
 
4.5%
0 871
 
3.7%
22 1
 
< 0.1%
30 1
 
< 0.1%
(Missing) 11
 
< 0.1%
ValueCountFrequency (%)
0 871
 
3.7%
1 1126
 
4.8%
2 8725
37.2%
3 7806
33.3%
4 1970
 
8.4%
5 1882
 
8.0%
6 1043
 
4.5%
22 1
 
< 0.1%
30 1
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
22 1
 
< 0.1%
6 1043
 
4.5%
5 1882
 
8.0%
4 1970
 
8.4%
3 7806
33.3%
2 8725
37.2%
1 1126
 
4.8%
0 871
 
3.7%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Memory size183.2 KiB
3.0
14238 
2.0
5479 
4.0
2439 
1.0
 
1270

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters70278
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 14238
60.8%
2.0 5479
 
23.4%
4.0 2439
 
10.4%
1.0 1270
 
5.4%
(Missing) 10
 
< 0.1%

Length

2024-09-20T17:13:25.204642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T17:13:25.293246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 14238
60.8%
2.0 5479
 
23.4%
4.0 2439
 
10.4%
1.0 1270
 
5.4%

Most occurring characters

ValueCountFrequency (%)
. 23426
33.3%
0 23426
33.3%
3 14238
20.3%
2 5479
 
7.8%
4 2439
 
3.5%
1 1270
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 23426
33.3%
0 23426
33.3%
3 14238
20.3%
2 5479
 
7.8%
4 2439
 
3.5%
1 1270
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 23426
33.3%
0 23426
33.3%
3 14238
20.3%
2 5479
 
7.8%
4 2439
 
3.5%
1 1270
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 23426
33.3%
0 23426
33.3%
3 14238
20.3%
2 5479
 
7.8%
4 2439
 
3.5%
1 1270
 
1.8%

YearsAtCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)0.2%
Missing13
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.0108867
Minimum0
Maximum40
Zeros740
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:25.410157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q310
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.1383942
Coefficient of variation (CV)0.87555176
Kurtosis3.8952933
Mean7.0108867
Median Absolute Deviation (MAD)3
Skewness1.7589009
Sum164216
Variance37.679883
MonotonicityNot monotonic
2024-09-20T17:13:25.576887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 3112
13.3%
1 2718
11.6%
3 2030
8.7%
2 2008
8.6%
10 1928
8.2%
4 1755
 
7.5%
7 1413
 
6.0%
9 1295
 
5.5%
8 1275
 
5.4%
6 1219
 
5.2%
Other values (27) 4670
19.9%
ValueCountFrequency (%)
0 740
 
3.2%
1 2718
11.6%
2 2008
8.6%
3 2030
8.7%
4 1755
7.5%
5 3112
13.3%
6 1219
 
5.2%
7 1413
6.0%
8 1275
5.4%
9 1295
5.5%
ValueCountFrequency (%)
40 15
 
0.1%
37 16
 
0.1%
36 32
 
0.1%
34 16
 
0.1%
33 85
0.4%
32 48
0.2%
31 48
0.2%
30 16
 
0.1%
29 30
 
0.1%
27 32
 
0.1%

YearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.1%
Missing15
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.2274455
Minimum0
Maximum22
Zeros3925
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:25.718156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum22
Range22
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6272842
Coefficient of variation (CV)0.85803216
Kurtosis0.48537702
Mean4.2274455
Median Absolute Deviation (MAD)3
Skewness0.91863465
Sum99011
Variance13.15719
MonotonicityNot monotonic
2024-09-20T17:13:25.826794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 5928
25.3%
0 3925
16.7%
7 3516
15.0%
3 2141
 
9.1%
4 1635
 
7.0%
8 1426
 
6.1%
9 1074
 
4.6%
1 887
 
3.8%
5 594
 
2.5%
6 591
 
2.5%
Other values (10) 1704
 
7.3%
ValueCountFrequency (%)
0 3925
16.7%
1 887
 
3.8%
2 5928
25.3%
3 2141
 
9.1%
4 1635
 
7.0%
5 594
 
2.5%
6 591
 
2.5%
7 3516
15.0%
8 1426
 
6.1%
9 1074
 
4.6%
ValueCountFrequency (%)
22 1
 
< 0.1%
18 32
 
0.1%
17 63
 
0.3%
16 110
 
0.5%
15 134
 
0.6%
14 176
 
0.8%
13 221
0.9%
12 149
 
0.6%
11 359
1.5%
10 459
2.0%

YearsSinceLastPromotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.1%
Missing11
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.1838207
Minimum0
Maximum17
Zeros9271
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:25.943408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum17
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2186147
Coefficient of variation (CV)1.4738457
Kurtosis3.6231184
Mean2.1838207
Median Absolute Deviation (MAD)1
Skewness1.9869325
Sum51156
Variance10.35948
MonotonicityNot monotonic
2024-09-20T17:13:26.060143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 9271
39.6%
1 5684
24.3%
2 2533
 
10.8%
7 1215
 
5.2%
4 961
 
4.1%
3 843
 
3.6%
5 713
 
3.0%
6 508
 
2.2%
11 377
 
1.6%
8 284
 
1.2%
Other values (7) 1036
 
4.4%
ValueCountFrequency (%)
0 9271
39.6%
1 5684
24.3%
2 2533
 
10.8%
3 843
 
3.6%
4 961
 
4.1%
5 713
 
3.0%
6 508
 
2.2%
7 1215
 
5.2%
8 284
 
1.2%
9 271
 
1.2%
ValueCountFrequency (%)
17 1
 
< 0.1%
15 206
 
0.9%
14 144
 
0.6%
13 158
 
0.7%
12 160
 
0.7%
11 377
 
1.6%
10 96
 
0.4%
9 271
 
1.2%
8 284
 
1.2%
7 1215
5.2%

YearsWithCurrManager
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.1%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.1275769
Minimum0
Maximum17
Zeros4197
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size183.2 KiB
2024-09-20T17:13:26.193307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5723794
Coefficient of variation (CV)0.8654907
Kurtosis0.16096225
Mean4.1275769
Median Absolute Deviation (MAD)3
Skewness0.83161992
Sum96705
Variance12.761895
MonotonicityNot monotonic
2024-09-20T17:13:26.293476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 5461
23.3%
0 4197
17.9%
7 3439
14.7%
3 2254
9.6%
8 1696
 
7.2%
4 1572
 
6.7%
1 1219
 
5.2%
9 1035
 
4.4%
5 496
 
2.1%
6 457
 
1.9%
Other values (8) 1603
 
6.8%
ValueCountFrequency (%)
0 4197
17.9%
1 1219
 
5.2%
2 5461
23.3%
3 2254
9.6%
4 1572
 
6.7%
5 496
 
2.1%
6 457
 
1.9%
7 3439
14.7%
8 1696
 
7.2%
9 1035
 
4.4%
ValueCountFrequency (%)
17 112
 
0.5%
16 32
 
0.1%
15 79
 
0.3%
14 79
 
0.3%
13 238
 
1.0%
12 277
 
1.2%
11 351
 
1.5%
10 435
 
1.9%
9 1035
4.4%
8 1696
7.2%

Employee Source
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing12
Missing (%)0.1%
Memory size183.2 KiB
Company Website
5400 
Seek
3689 
Indeed
2529 
Jora
2422 
LinkedIn
2339 
Other values (7)
7045 

Length

Max length15
Median length9
Mean length8.5622865
Min length1

Characters and Unicode

Total characters200563
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowReferral
2nd rowReferral
3rd rowReferral
4th rowReferral
5th rowReferral

Common Values

ValueCountFrequency (%)
Company Website 5400
23.0%
Seek 3689
15.7%
Indeed 2529
10.8%
Jora 2422
10.3%
LinkedIn 2339
10.0%
Recruit.net 2322
9.9%
GlassDoor 2176
9.3%
Adzuna 2126
 
9.1%
Referral 418
 
1.8%
Test 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 12
 
0.1%

Length

2024-09-20T17:13:26.427077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
company 5400
18.7%
website 5400
18.7%
seek 3689
12.8%
indeed 2529
8.8%
jora 2422
8.4%
linkedin 2339
8.1%
recruit.net 2322
8.1%
glassdoor 2176
7.5%
adzuna 2126
 
7.4%
referral 418
 
1.5%
Other values (3) 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 31056
15.5%
n 17055
 
8.5%
a 12542
 
6.3%
o 12174
 
6.1%
i 10061
 
5.0%
t 10045
 
5.0%
s 9753
 
4.9%
d 9523
 
4.7%
r 7756
 
3.9%
k 6028
 
3.0%
Other values (25) 74570
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 31056
15.5%
n 17055
 
8.5%
a 12542
 
6.3%
o 12174
 
6.1%
i 10061
 
5.0%
t 10045
 
5.0%
s 9753
 
4.9%
d 9523
 
4.7%
r 7756
 
3.9%
k 6028
 
3.0%
Other values (25) 74570
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 31056
15.5%
n 17055
 
8.5%
a 12542
 
6.3%
o 12174
 
6.1%
i 10061
 
5.0%
t 10045
 
5.0%
s 9753
 
4.9%
d 9523
 
4.7%
r 7756
 
3.9%
k 6028
 
3.0%
Other values (25) 74570
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 31056
15.5%
n 17055
 
8.5%
a 12542
 
6.3%
o 12174
 
6.1%
i 10061
 
5.0%
t 10045
 
5.0%
s 9753
 
4.9%
d 9523
 
4.7%
r 7756
 
3.9%
k 6028
 
3.0%
Other values (25) 74570
37.2%

Interactions

2024-09-20T17:13:12.610212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:54.598083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:55.930027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:57.405807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:58.929830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:00.356857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:01.776588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:03.293363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:05.037161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:06.605230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:08.062850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:09.675931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:11.076681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:12.726735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:54.688386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:56.060791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:57.493595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:59.043457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:00.479211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:01.876919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:03.393420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:05.129107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:06.713185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:08.163337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:09.760533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:11.176673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:12.843426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:54.793497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:56.187939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:57.610265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:59.163130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:00.602499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:01.977025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:03.513463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:05.246312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:06.829490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:08.278884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:09.886300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:11.293635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:12.948725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:54.929118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:56.293309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:57.705954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:59.275239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:00.705958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:02.087909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:03.726344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:05.346030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:06.926789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:08.393764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:09.976733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:11.434598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:13.059952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:55.046332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:56.403930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:57.810384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:59.376991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:00.810208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:02.193683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:03.864924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:05.460283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:07.069038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:08.659645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:10.092812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:11.547519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:13.177000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:55.128980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:56.507060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:57.909966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:59.485339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:00.910160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:02.293604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:03.972006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:05.581075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:07.160700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:08.760585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:10.193485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:11.647649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:13.276712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:55.229092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:56.610208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:58.007813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:59.611533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:01.035519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:02.376736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:04.060377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:05.676614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:07.290807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:08.860144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:10.310111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:11.755117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:13.549463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:55.310497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:56.710278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:58.226862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:59.706954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:01.127146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:02.486981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:04.256135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:05.794717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:07.393605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:08.960160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:10.426718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:11.860116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:13.677001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:55.427385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:56.830426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:58.360758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:59.825754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:01.242842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:02.611846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:04.440510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:05.905480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:07.512362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:09.096357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:10.552796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:11.976690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:13.785697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:55.513928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:56.945823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:58.463951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:59.934611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:01.343937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:02.738499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:04.543543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:06.029509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:07.620048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:09.228918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:10.647044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:12.093385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:13.893867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:55.627179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:57.060574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:58.563830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:00.055184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:01.443487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:02.846090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:04.692090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:06.179077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:07.729357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:09.360600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:10.760141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:12.310422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:14.010104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:55.719053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:57.176689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:58.663386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:00.143390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:01.560094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:03.077324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:04.818794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:06.326270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:07.827057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:09.460667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:10.860017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:12.410587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:14.126741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:55.823021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:57.290621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:12:58.785794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:00.262212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:01.660028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:03.187927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:04.930357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:06.474669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:07.944406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:09.560483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:10.972734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T17:13:12.510565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-20T17:13:26.543694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeAttritionBusinessTravelDailyRateDepartmentEducationEducationFieldEmployee SourceEnvironmentSatisfactionGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
Age1.0000.2260.0870.0100.0500.2050.0380.048-0.0090.028-0.0070.1580.0950.0400.0740.0240.0540.0000.0410.0610.0130.0470.0000.0880.3060.0130.0430.1070.0890.0760.084
Attrition0.2261.0000.1290.0960.0870.0440.0620.0470.0000.0020.0000.1060.1110.0570.1260.0460.0000.0000.1470.0690.0000.0380.0000.1380.1080.0100.0280.0790.0980.0440.081
BusinessTravel0.0870.1291.0000.0820.0050.0460.0410.0490.0120.0220.0140.0280.0440.0280.0400.0530.0140.0120.0270.0740.0130.0300.0140.0310.0480.0140.0220.0480.0380.0620.056
DailyRate0.0100.0960.0821.0000.063-0.0160.0450.0460.0070.0310.0090.0540.0430.0270.057-0.0130.0160.0000.0290.0650.0250.0550.0000.0460.003-0.0290.056-0.029-0.022-0.033-0.032
Department0.0500.0870.0050.0631.0000.5780.6220.5780.7070.5780.7070.0950.6880.4090.4090.0260.7070.7070.5000.5800.5770.0170.7070.4090.0400.5770.0260.0290.5780.5780.042
Education0.2050.0440.046-0.0160.5781.0000.4490.450-0.0250.5770.0200.0510.4490.3540.410-0.0060.0330.7070.5000.4520.5770.0310.7070.3540.085-0.0030.0400.0380.0390.0240.033
EducationField0.0380.0620.0410.0450.6220.4491.0000.3790.7070.5770.7070.0360.3840.3540.4080.0230.7070.7070.5000.3790.5770.0290.7070.3540.0260.5780.0210.0290.3790.3790.035
Employee Source0.0480.0470.0490.0460.5780.4500.3791.0001.0000.8161.0000.0670.4520.5040.5820.0581.0001.0000.7080.4340.8170.0721.0000.5070.0720.8170.0690.0680.3390.3380.075
EnvironmentSatisfaction-0.0090.0000.0120.0070.707-0.0250.7071.0001.0001.000-0.0170.0181.0001.0001.0000.027-0.0050.7501.0001.0001.0000.0081.0001.000-0.0160.0020.000-0.0000.0020.014-0.010
Gender0.0280.0020.0220.0310.5780.5770.5770.8161.0001.0001.0000.0430.8180.5780.5780.0321.0001.0000.7080.8180.8170.0201.0000.5770.0400.8170.0130.0560.5800.5780.044
JobInvolvement-0.0070.0000.0140.0090.7070.0200.7071.000-0.0171.0001.0000.0091.0000.7070.707-0.0110.0101.0000.7071.0001.0000.0001.0000.7070.0020.0050.0000.0080.010-0.0090.031
JobLevel0.1580.1060.0280.0540.0950.0510.0360.0670.0180.0430.0091.0000.5670.0330.0560.0750.0090.0180.0200.0890.0140.0470.0090.0710.5330.0440.0360.3540.2470.2180.239
JobRole0.0950.1110.0440.0430.6880.4490.3840.4521.0000.8181.0000.5671.0000.5030.5820.0551.0001.0000.7080.4550.8170.0761.0000.5040.2780.8170.0750.1850.3590.3550.128
JobSatisfaction0.0400.0570.0280.0270.4090.3540.3540.5041.0000.5780.7070.0330.5031.0000.5780.0740.7071.0000.7080.5060.5770.0240.7070.5000.0700.5780.0340.0530.3590.3570.059
MaritalStatus0.0740.1260.0400.0570.4090.4100.4080.5821.0000.5780.7070.0560.5820.5781.0000.0430.7071.0000.7070.5810.5770.0410.7070.7350.0810.5780.0320.0610.4120.4140.060
MonthlyRate0.0240.0460.053-0.0130.026-0.0060.0230.0580.0270.032-0.0110.0750.0550.0740.0431.0000.0060.0000.0490.0830.0200.0890.0040.0450.015-0.0100.079-0.0190.001-0.012-0.025
NumCompaniesWorked0.0540.0000.0140.0160.7070.0330.7071.000-0.0051.0000.0100.0091.0000.7070.7070.0061.0001.0000.7071.0001.0000.0001.0000.7070.063-0.0050.000-0.127-0.082-0.052-0.101
Over180.0000.0000.0120.0000.7070.7070.7071.0000.7501.0001.0000.0181.0001.0001.0000.0001.0001.0001.0001.0001.0000.0081.0001.0000.0091.0000.0000.0000.7070.7070.032
OverTime0.0410.1470.0270.0290.5000.5000.5000.7081.0000.7080.7070.0200.7080.7080.7070.0490.7071.0001.0000.7090.7070.0350.7070.7080.0390.7080.0240.0560.5040.5010.046
PercentSalaryHike0.0610.0690.0740.0650.5800.4520.3790.4341.0000.8181.0000.0890.4550.5060.5810.0831.0001.0000.7091.0000.8640.1031.0000.5030.0970.8180.0790.0870.3470.3480.091
PerformanceRating0.0130.0000.0130.0250.5770.5770.5770.8171.0000.8171.0000.0140.8170.5770.5770.0201.0001.0000.7070.8641.0000.0081.0000.5770.0170.8160.0130.0140.5780.5780.037
RelationshipSatisfaction0.0470.0380.0300.0550.0170.0310.0290.0720.0080.0200.0000.0470.0760.0240.0410.0890.0000.0080.0350.1030.0081.0000.0000.0550.0840.0140.0380.0620.0810.0890.053
StandardHours0.0000.0000.0140.0000.7070.7070.7071.0001.0001.0001.0000.0091.0000.7070.7070.0041.0001.0000.7071.0001.0000.0001.0000.7070.0001.0000.0000.0000.7070.7070.033
StockOptionLevel0.0880.1380.0310.0460.4090.3540.3540.5071.0000.5770.7070.0710.5040.5000.7350.0450.7071.0000.7080.5030.5770.0550.7071.0000.0850.5770.0460.0660.3570.3610.074
TotalWorkingYears0.3060.1080.0480.0030.0400.0850.0260.072-0.0160.0400.0020.5330.2780.0700.0810.0150.0630.0090.0390.0970.0170.0840.0000.0851.000-0.0130.0720.5940.4930.3350.499
TrainingTimesLastYear0.0130.0100.014-0.0290.577-0.0030.5780.8170.0020.8170.0050.0440.8170.5780.578-0.010-0.0051.0000.7080.8180.8160.0141.0000.577-0.0131.0000.0080.0020.0050.014-0.009
WorkLifeBalance0.0430.0280.0220.0560.0260.0400.0210.0690.0000.0130.0000.0360.0750.0340.0320.0790.0000.0000.0240.0790.0130.0380.0000.0460.0720.0081.0000.0780.0640.0670.084
YearsAtCompany0.1070.0790.048-0.0290.0290.0380.0290.068-0.0000.0560.0080.3540.1850.0530.061-0.019-0.1270.0000.0560.0870.0140.0620.0000.0660.5940.0020.0781.0000.8540.5220.845
YearsInCurrentRole0.0890.0980.038-0.0220.5780.0390.3790.3390.0020.5800.0100.2470.3590.3590.4120.001-0.0820.7070.5040.3470.5780.0810.7070.3570.4930.0050.0640.8541.0000.5060.727
YearsSinceLastPromotion0.0760.0440.062-0.0330.5780.0240.3790.3380.0140.578-0.0090.2180.3550.3570.414-0.012-0.0520.7070.5010.3480.5780.0890.7070.3610.3350.0140.0670.5220.5061.0000.471
YearsWithCurrManager0.0840.0810.056-0.0320.0420.0330.0350.075-0.0100.0440.0310.2390.1280.0590.060-0.025-0.1010.0320.0460.0910.0370.0530.0330.0740.499-0.0090.0840.8450.7270.4711.000

Missing values

2024-09-20T17:13:14.352338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-20T17:13:14.943853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-20T17:13:15.877063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberApplication IDEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerEmployee Source
041.0Voluntary ResignationTravel_Rarely1102.0Sales12.0Life Sciences111234562.0Female943.02.0Sales Executive4Single599319479.08.0YYes113.01.080.00.08.00.01.06.04.00.05.0Referral
141.0Voluntary ResignationTravel_Rarely1102.0Sales12.0Life Sciences111234582.0Female943.02.0Sales Executive4Single599319479.04.0YYes113.01.080.00.08.00.01.06.04.00.05.0Referral
241.0Voluntary ResignationTravel_Rarely1102.0Sales12.0Life Sciences171234622.0Female943.02.0Sales Executive4Single599319479.08.0YYes113.01.080.00.08.00.01.06.04.00.05.0Referral
341.0Voluntary ResignationTravel_Rarely1102.0Sales12.0Life Sciences181234632.0Female943.02.0Sales Executive4Single599319479.04.0YYes113.01.080.00.08.00.01.06.04.00.05.0Referral
441.0Voluntary ResignationTravel_Rarely1102.0Sales12.0Life Sciences191234642.0Female943.02.0Sales Executive4Single599319479.08.0YYes113.01.080.00.08.00.01.06.04.00.05.0Referral
541.0Voluntary ResignationTravel_Rarely1102.0Sales12.0Life Sciences1101234654.0Female333.04.0Manager3Divorced1475619730.02.0YYes143.03.080.03.021.02.03.05.00.00.02.0Company Website
641.0Voluntary ResignationTravel_Rarely1102.0Sales12.0Life Sciences1111234661.0Female413.05.0Manager1Married195663854.05.0YNo113.04.080.00.033.05.01.029.08.011.010.0Indeed
741.0Voluntary ResignationTravel_Rarely1102.0Sales12.0Life Sciences1131234682.0Female943.02.0Sales Executive4Single599319479.04.0YYes113.01.080.00.08.00.01.06.04.00.05.0Referral
841.0Voluntary ResignationTravel_Rarely1102.0Sales12.0Life Sciences1171234722.0Female943.02.0Sales Executive4Single599319479.08.0YYes113.01.080.00.08.00.01.06.04.00.05.0Referral
941.0Voluntary ResignationTravel_Rarely1102.0Sales12.0Life Sciences1181234732.0Female943.02.0Sales Executive4Single599319479.04.0YYes113.01.080.00.08.00.01.06.04.00.05.0Referral
AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberApplication IDEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerEmployee Source
2342660.0Current employeeTravel_Rarely370.0Research & Development1.04.0Life Sciences119332142787.03.0Male921.03.0Healthcare Representative4Divorced1088320467.00.0YNo203.03.080.01.019.02.04.01.00.00.00.0Company Website
2342760.0Current employeeTravel_Rarely370.0Research & Development1.04.0Medical119336142791.03.0Male921.03.0Healthcare Representative4Single1088320467.03.0YNo204.03.080.01.020.02.03.020.07.02.013.0Company Website
2342860.0Current employeeTravel_Rarely370.0Research & Development1.04.0Life Sciences119337142792.03.0Male921.03.0Healthcare Representative4Divorced1088320467.00.0YNo204.03.080.01.019.02.04.01.00.00.00.0Company Website
2342960.0Current employeeTravel_Rarely370.0Research & Development1.04.0Medical119338142793.03.0Male921.03.0Healthcare Representative4Divorced1088320467.03.0YNo203.03.080.01.019.02.04.01.00.00.00.0Company Website
2343060.0Current employeeTravel_Rarely370.0Research & Development1.04.0Life Sciences119340142795.03.0Male921.03.0Healthcare Representative4Divorced1088320467.00.0YNo203.03.080.01.019.02.04.01.00.00.00.0Company Website
2343160.0Current employeeTravel_Rarely370.0Research & Development1.04.0Medical119344142799.03.0Male921.03.0Healthcare Representative4Single1088320467.03.0YNo204.03.080.01.020.02.03.020.07.02.013.0Company Website
2343260.0Current employeeTravel_Rarely370.0Research & Development1.04.0Life Sciences119345142800.03.0Male921.03.0Healthcare Representative4Divorced1088320467.00.0YNo204.03.080.01.019.02.04.01.00.00.00.0Company Website
23433NaNVoluntary ResignationTravel_Frequently1009.0Research & Development1.03.0Life Sciences116794140249.04.0Male833.02.0Sales Executive3Married53012939.04.0YNo153.03.080.02.04.02.02.02.01.02.02.0Adzuna
23434NaNCurrent employeeTravel_Rarely1354.0Research & Development5.03.0Medical11956125411.03.0Female452.03.0Manager1Single116315615.02.0YNo123.04.080.00.014.06.03.011.010.05.08.0Indeed
23435NaNCurrent employeeNon-Travel1142.0Research & Development8.02.0Life Sciences117587141042.04.0Male723.02.0Healthcare Representative4Divorced40698841.03.0YYes183.03.080.00.08.02.03.02.02.02.02.0Recruit.net

Duplicate rows

Most frequently occurring

AgeAttritionBusinessTravelDailyRateDepartmentEducationEducationFieldEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerEmployee Source# duplicates
021.0Current employeeTravel_Rarely391.0Research & Development2.0Life Sciences8161.0Male391.01.0Laboratory Technician3Single229310558.01.0YNo124.03.080.00.01.02.02.01.00.00.01.0Seek2
121.0Current employeeTravel_Rarely391.0Research & Development2.0Life Sciences8193.0Male391.01.0Laboratory Technician3Single22937324.01.0YNo123.03.080.00.01.02.02.01.00.00.01.0Seek2
221.0Current employeeTravel_Rarely391.0Research & Development2.0Medical8171.0Male391.01.0Laboratory Technician3Single229310558.01.0YNo123.03.080.00.01.02.02.01.00.00.01.0Seek2
326.0Voluntary ResignationTravel_Rarely1357.0Research & Development3.0Life Sciences8131.0Male481.01.0Laboratory Technician3Single229321534.01.0YNo123.03.080.00.01.02.02.01.00.00.01.0Seek2
426.0Voluntary ResignationTravel_Rarely1357.0Research & Development3.0Life Sciences8151.0Male481.01.0Laboratory Technician3Single229310558.01.0YNo123.03.080.00.01.02.02.01.00.00.01.0Seek2
526.0Voluntary ResignationTravel_Rarely1357.0Research & Development3.0Technical Degree8141.0Male481.01.0Laboratory Technician3Single229326009.01.0YNo123.03.080.00.01.02.02.01.00.00.01.0Seek2
626.0Voluntary ResignationTravel_Rarely1357.0Research & Development3.0Technical Degree8181.0Male481.01.0Laboratory Technician3Single229310558.01.0YNo123.03.080.00.01.02.02.01.00.00.01.0Seek2
732.0Current employeeNon-Travel953.0Research & Development4.0Life Sciences232444.0Female1002.02.0Sales Executive4Married665214369.05.0YNo133.01.080.01.08.02.02.06.03.00.00.0Seek2
837.0Voluntary ResignationTravel_Rarely807.0Human Resources4.0Human Resources11.0Female373.02.0Sales Executive4Single599319479.08.0YYes114.01.080.00.08.00.01.06.04.00.05.0Referral2
937.0Voluntary ResignationTravel_Rarely807.0Human Resources4.0Human Resources51.0Female373.02.0Sales Executive4Single599319479.08.0YYes113.01.080.00.08.00.01.06.04.00.05.0Referral2